Remote visual analysis of large turbulence databases at multiple scales
The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visua...
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Published in: | Journal of parallel and distributed computing Vol. 120; pp. 115 - 126 |
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Abstract | The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. We present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction.
•Data-level wavelet compression reduces bandwidth, memory and compute footprint.•Latency is improved between database components and multi-user support is introduced.•Remote visualization is enabled on a large database cluster using commodity hardware.•New wavelet analysis tools are demonstrated for existing turbulence data cluster. |
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AbstractList | The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. In this paper, we present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. Finally, the database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction. The remote analysis and visualization of raw large turbulence datasets is challenging. Current accurate direct numerical simulations (DNS) of turbulent flows generate datasets with billions of points per time-step and several thousand time-steps per simulation. Until recently, the analysis and visualization of such datasets was restricted to scientists with access to large supercomputers. The public Johns Hopkins Turbulence database simplifies access to multi-terabyte turbulence datasets and facilitates the computation of statistics and extraction of features through the use of commodity hardware. We present a framework designed around wavelet-based compression for high-speed visualization of large datasets and methods supporting multi-resolution analysis of turbulence. By integrating common technologies, this framework enables remote access to tools available on supercomputers and over 230 terabytes of DNS data over the Web. The database toolset is expanded by providing access to exploratory data analysis tools, such as wavelet decomposition capabilities and coherent feature extraction. •Data-level wavelet compression reduces bandwidth, memory and compute footprint.•Latency is improved between database components and multi-user support is introduced.•Remote visualization is enabled on a large database cluster using commodity hardware.•New wavelet analysis tools are demonstrated for existing turbulence data cluster. |
Author | Hamann, Bernd Canada, Curtis Livescu, Daniel Burns, Randal Kanov, Kalin Ahrens, James Pulido, Jesus |
Author_xml | – sequence: 1 givenname: Jesus surname: Pulido fullname: Pulido, Jesus email: jpulido@ucdavis.edu organization: Department of Computer Science, University of California, Davis, CA 95616, USA – sequence: 2 givenname: Daniel orcidid: 0000-0003-2367-1547 surname: Livescu fullname: Livescu, Daniel email: livescu@lanl.gov organization: Los Alamos National Laboratory, Los Alamos, NM 87544, USA – sequence: 3 givenname: Kalin orcidid: 0000-0002-6323-9081 surname: Kanov fullname: Kanov, Kalin email: kalin@cs.jhu.edu organization: Johns Hopkins University, Baltimore, MD 21218, USA – sequence: 4 givenname: Randal surname: Burns fullname: Burns, Randal email: randal@cs.jhu.edu organization: Johns Hopkins University, Baltimore, MD 21218, USA – sequence: 5 givenname: Curtis surname: Canada fullname: Canada, Curtis email: cvc@lanl.gov organization: Los Alamos National Laboratory, Los Alamos, NM 87544, USA – sequence: 6 givenname: James surname: Ahrens fullname: Ahrens, James email: ahrens@lanl.gov organization: Los Alamos National Laboratory, Los Alamos, NM 87544, USA – sequence: 7 givenname: Bernd surname: Hamann fullname: Hamann, Bernd email: hamann@cs.ucdavis.edu organization: Department of Computer Science, University of California, Davis, CA 95616, USA |
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CitedBy_id | crossref_primary_10_1016_j_cag_2020_03_002 crossref_primary_10_1016_j_paerosci_2019_04_004 crossref_primary_10_1088_1742_6596_1290_1_012008 crossref_primary_10_1111_cgf_13671 crossref_primary_10_1109_TVCG_2020_3030381 |
Cites_doi | 10.1146/annurev.fl.24.010192.002143 10.1109/TVCG.2006.186 10.1098/rsta.2012.0185 10.1109/TVCG.2014.2346458 10.1017/S002211200800092X 10.1109/38.865875 10.1109/78.157290 10.1080/14685240500149831 10.1109/TVCG.2012.274 10.1109/TVCG.2009.69 10.1017/S0022112010000571 10.1017/S0022112008001481 10.1016/j.compfluid.2015.11.001 |
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Keywords | Wavelets Turbulence Databases Distributed systems Data reduction Remote visualization |
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Fluid Mech. doi: 10.1017/S0022112008001481 contributor: fullname: Livescu – volume: 125 start-page: 39 year: 2016 ident: 10.1016/j.jpdc.2018.05.011_b23 article-title: Survey and analysis of multiresolution methods for turbulence data publication-title: Comput. & Fluids doi: 10.1016/j.compfluid.2015.11.001 contributor: fullname: Pulido – start-page: 163 year: 2006 ident: 10.1016/j.jpdc.2018.05.011_b6 article-title: Remote large data visualization in the paraview framework contributor: fullname: Cedilnik |
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SubjectTerms | Computer Science Data Reduction Distributed Systems MATHEMATICS AND COMPUTING Remote Visualization Turbulence Wavelets |
Title | Remote visual analysis of large turbulence databases at multiple scales |
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